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## What changes were proposed in this pull request? CompactibleFileStreamLog relys on "compactInterval" to detect a compaction batch. If the "compactInterval" is reset by user, CompactibleFileStreamLog will return wrong answer, resulting data loss. This PR procides a way to check the validity of 'compactInterval', and calculate an appropriate value. ## How was this patch tested? When restart a stream, we change the 'spark.sql.streaming.fileSource.log.compactInterval' different with the former one. The primary solution to this issue was given by uncleGen Added extensions include an additional metadata field in OffsetSeq and CompactibleFileStreamLog APIs. zsxwing Author: Tyson Condie <tcondie@gmail.com> Author: genmao.ygm <genmao.ygm@genmaoygmdeMacBook-Air.local> Closes #15852 from tcondie/spark-18187. |
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README.md |
Spark SQL
This module provides support for executing relational queries expressed in either SQL or the DataFrame/Dataset API.
Spark SQL is broken up into four subprojects:
- Catalyst (sql/catalyst) - An implementation-agnostic framework for manipulating trees of relational operators and expressions.
- Execution (sql/core) - A query planner / execution engine for translating Catalyst's logical query plans into Spark RDDs. This component also includes a new public interface, SQLContext, that allows users to execute SQL or LINQ statements against existing RDDs and Parquet files.
- Hive Support (sql/hive) - Includes an extension of SQLContext called HiveContext that allows users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allows users to run queries that include Hive UDFs, UDAFs, and UDTFs.
- HiveServer and CLI support (sql/hive-thriftserver) - Includes support for the SQL CLI (bin/spark-sql) and a HiveServer2 (for JDBC/ODBC) compatible server.